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on Econometric Time Series |
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Issue of 2025–06–30
eighteen papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Iacone, Fabrizio; Taylor, AM Robert |
| Abstract: | We propose a nonparametric portmanteau test for detecting changes in the unconditional mean of a univariate time series which may display either long or short memory. Our approach is designed to have power against, among other things, cases where the mean component of the series displays abrupt level shifts, deterministic trending behaviour, or is subject to some form of time-varying, continuous change. The test we propose is simple to compute, being based on ratios of periodogram ordinates, has a pivotal limiting null distribution of known form which reduces to the multiple of a χ²₂ random variable in the case where the series is short memory, and has power against a wide class of time-varying mean models. A Monte Carlo simulation study into the finite sample behaviour of the test shows it to have both good size properties under the null for a range of long and short memory series and to exhibit good power against a variety of plausible time-varying mean alternatives. Because of its simplicity, we recommend our periodogram ratio test as a routine portmanteau test for whether the mean component of a time series can reasonably be treated as constant. |
| Date: | 2025–06–19 |
| URL: | https://d.repec.org/n?u=RePEc:esy:uefcwp:41128 |
| By: | Joann Jasiak; Cheng Zhong |
| Abstract: | We study the Functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account. The Karhunen-Loeve (KL) dynamic factor model is introduced to bridge the functional and discrete time dynamic models. It offers a convenient framework for functional time series analysis. For intraday forecasting, we introduce a new algorithm based on the FPCA applied by rolling, which can be used for any data observed continuously 24/7. The proposed FPCA forecasting methods are applied to return functions computed from data sampled hourly and at 15-minute intervals. Next, the functional forecasts evaluated at discrete points in time are compared with the forecasts based on other methods, including machine learning and a traditional ARMA model. The proposed FPCA-based methods perform well in terms of forecast accuracy and outperform competitors in terms of directional (sign) of return forecasts at fixed points in time. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.20508 |
| By: | Dimitris Korobilis |
| Abstract: | I introduce a high-dimensional Bayesian vector autoregressive (BVAR) framework designed to estimate the effects of conventional monetary policy shocks. The model captures structural shocks as latent factors, enabling computationally efficient estimation in high-dimensional settings through a straightforward Gibbs sampler. By incorporating time variation in the effects of monetary policy while maintaining tractability, the methodology offers a flexible and scalable approach to empirical macroeconomic analysis using BVARs, well-suited to handle data irregularities observed in recent times. Applied to the U.S. economy, I identify monetary shocks using a combination of high-frequency surprises and sign restrictions, yielding results that are robust across a wide range of specification choices. The findings indicate that the Federal Reserve's influence on disaggregated consumer prices fluctuated significantly during the 2022-24 high-inflation period, shedding new light on the evolving dynamics of monetary policy transmission. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.06649 |
| By: | Juvonen, Petteri; Lindblad, Annika |
| Abstract: | We analyse the accuracy of an econometric model for nowcasting GDP growth in a true real-time setting. The analysis is based on a unique sample of nowcasts that were produced in real time and stored. Our results support the use of econometric models for nowcasting because the accuracy of these real-time nowcasts is found to be comparable to the first GDP estimates of the statistical authority. The nowcasts are produced by a large Bayesian vector autoregressive model. We find the model fares well against other statistical models, and the results suggest that its performance has been more robust to COVID-19 fluctuations than that of a dynamic factor model. We also analyse comments on the nowcast tweets published on Twitter in real time. |
| Keywords: | Nowcasting, Real-time analysis, Vector autoregressions, Bayesian methods, Mixed frequency, Business cycles |
| JEL: | C11 C52 C53 E32 E37 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bofrdp:319609 |
| By: | Sofia Velasco |
| Abstract: | This article proposes a novel framework that integrates Bayesian Additive Regression Trees (BART) into a Factor-Augmented Vector Autoregressive (FAVAR) model to forecast macro-financial variables and examine asymmetries in the transmission of oil price shocks. By employing nonparametric techniques for dimension reduction, the model captures complex, nonlinear relationships between observables and latent factors that are often missed by linear approaches. A simulation experiment comparing FABART to linear alternatives and a Monte Carlo experiment demonstrate that the framework accurately recovers the relationship between latent factors and observables in the presence of nonlinearities, while remaining consistent under linear data-generating processes. The empirical application shows that FABART substantially improves forecast accuracy for industrial production relative to linear benchmarks, particularly during periods of heightened volatility and economic stress. In addition, the model reveals pronounced sign asymmetries in the transmission of oil supply news shocks to the U.S. economy, with positive shocks generating stronger and more persistent contractions in real activity and inflation than the expansions triggered by negative shocks. A similar pattern emerges at the U.S. federal state level, where negative shocks lead to modest declines in employment compared to the substantially larger contractions observed after positive shocks. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.11551 |
| By: | Miguel D. Ramirez (Department of Economics, Trinity College) |
| Keywords: | Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS), economic output, Gregory-Hansen cointegration single-break test, public capital stock, Johansen Cointegration test, labor productivity, KPSS no unit root test, single-break (Zivot-Andrews) unit root test, and vector error correction model (VECM). |
| JEL: | C22 O40 O54 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:tri:wpaper:2502 |
| By: | Hyeon-seung Huh (Yonsei University); David Kim (University of Sydney) |
| Abstract: | In the sign-identified Bayesian SVAR model, the standard setup usually postulates a Haar prior for the rotation matrix. However, the rotation matrix does not enter the likelihood, and its prior is never updated by data. A key implication is that the Haar prior rotation matrix can be unintentionally informative about posterior inference, despite having no relationship with economic interpretations or data. We show empirically how Haar prior rotation matrix could affect the results in the context of two well-known models: Baumeister and Hamilton (2018) and Peersman and Straub (2004, 2009). For both models, the histograms of accepted impact responses are shown to reflect closely the histograms of accepted rotation matrices. Although sampling uncertainty is updated by the data, it barely contributes to determining the set of accepted impact responses compared to the uncertainty about the rotation matrix, explaining why the histograms between the accepted impact responses and the accepted rotation matrices are similar in shape. To a lesser extent, the influence of the rotation matrix is carried over to subsequent responses where additional sampling uncertainty arises. Our results reinforce the argument that the rotation prior can affect the distribution of accepted responses, possibly leading to erroneous inferences. |
| Keywords: | Structural vector autoregressions, Sign restrictions, Haar prior, Rotation matrix, Informativeness |
| JEL: | C32 C36 C51 E32 E52 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2025rwp-246 |
| By: | Austin Pollok |
| Abstract: | The discrepancy between realized volatility and the market's view of volatility has been known to predict individual equity options at the monthly horizon. It is not clear how this predictability depends on a forecast's ability to predict firm-level volatility. We consider this phenomenon at the daily frequency using high-dimensional machine learning models, as well as low-dimensional factor models. We find that marginal improvements to standard forecast error measurements can lead to economically significant gains in portfolio performance. This makes a case for re-imagining the way we train models that are used to construct portfolios. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.07928 |
| By: | Hyeon-seung Huh (Yonsei University); David Kim (University of Sydney) |
| Abstract: | The use of sign restrictions to identify monetary policy shocks in structural vector autoregression (SVAR) models has garnered significant attention in recent years. In this context, we revisit two influential studies—Uhlig (2005) and Arias et al. (2019)—which offer conflicting conclusions regarding the output effects of contractionary monetary policy shocks. Our analysis seeks to uncover the underlying causes of these discrepancies and evaluate the sensitivity of the results to alternative model specifications. Specifically, we examine four key factors: (i) the influence of rotation priors on posterior inference in sign-restricted SVAR models, (ii) the robustness of findings when employing an alternative algorithm to generate large sets of responses, (iii) the sensitivity of results to variations in identifying restrictions, and (iv) the robustness of conclusions to changes in the monetary policy equation and the inclusion of the Great Moderation. |
| Keywords: | Sign restrictions, Rotation matrix, monetary policy shocks, Structural vectorvautoregression, Baumeister and Hamilton critique |
| JEL: | C32 C51 E32 E52 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2025rwp-245 |
| By: | Jan Ditzen; Ovidijus Stauskas |
| Abstract: | Information criteria (IC) are important tools in the literature of factor models that allow one to estimate a typically unknown number of latent factors. Although first proposed for the Principal Components setting in the seminal work by Bai and Ng (2002), it has recently been shown that IC perform extremely well in Common Correlated Effects (CCE) and related setups with stationary factors. In particular, they can consistently select a sufficient set of cross-section averages (CAs) to approximate the factor space. Given that CAs can proxy nonstationary factors, it is tempting to believe that the consistency of IC continues to hold under such generality. This study is a cautionary tale for practitioners. We demonstrate formally and in simulations that IC has a severe underselection issue even under very mild forms of factor non-stationarity, which goes against the sentiment in the CAs literature. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.08615 |
| By: | Xin Tian |
| Abstract: | This report presents a comprehensive evaluation of three Value-at-Risk (VaR) modeling approaches: Historical Simulation (HS), GARCH with Normal approximation (GARCH-N), and GARCH with Filtered Historical Simulation (FHS), using both in-sample and multi-day forecasting frameworks. We compute daily 5 percent VaR estimates using each method and assess their accuracy via empirical breach frequencies and visual breach indicators. Our findings reveal severe miscalibration in the HS and GARCH-N models, with empirical breach rates far exceeding theoretical levels. In contrast, the FHS method consistently aligns with theoretical expectations and exhibits desirable statistical and visual behavior. We further simulate 5-day cumulative returns under both GARCH-N and GARCH-FHS frameworks to compute multi-period VaR and Expected Shortfall. Results show that GARCH-N underestimates tail risk due to its reliance on the Gaussian assumption, whereas GARCH-FHS provides more robust and conservative tail estimates. Overall, the study demonstrates that the GARCH-FHS model offers superior performance in capturing fat-tailed risks and provides more reliable short-term risk forecasts. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.05646 |
| By: | Aryan Singh; Paul O Reilly; Daim Sharif; Patrick Haughey; Eoghan McCarthy; Sathvika Thorali Suresh; Aakhil Anvar; Adarsh Sajeev Kumar |
| Abstract: | A multivariate risk analysis for VaR and CVaR using different copula families is performed on historical financial time series fitted with DCC-GARCH models. A theoretical background is provided alongside a comparison of goodness-of-fit across different copula families to estimate the validity and effectiveness of approaches discussed. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.06950 |
| By: | Sukru Selim Calik; Andac Akyuz; Zeynep Hilal Kilimci; Kerem Colak |
| Abstract: | Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer-based time series models with explainable artificial intelligence (XAI) to enhance the interpretability and accuracy of stock price predictions. The analysis focuses on the daily stock prices of the five highest-volume banks listed in the BIST100 index, along with XBANK and XU100 indices, covering the period from January 2015 to March 2025. Models including DLinear, LTSNet, Vanilla Transformer, and Time Series Transformer are employed, with input features enriched by technical indicators. SHAP and LIME techniques are used to provide transparency into the influence of individual features on model outputs. The results demonstrate the strong predictive capabilities of transformer models and highlight the potential of interpretable machine learning to empower individuals in making informed investment decisions and actively engaging in financial markets. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.06345 |
| By: | Mindy L. Mallory; Rundong Peng; Meilin Ma; H. Holly Wang |
| Abstract: | Price transmission has been studied extensively in agricultural economics through the lens of spatial and vertical price relationships. Classical time series econometric techniques suffer from the "curse of dimensionality" and are applied almost exclusively to small sets of price series, either prices of one commodity in a few regions or prices of a few commodities in one region. However, an agrifood supply chain usually contains several commodities (e.g., cattle and beef) and spans numerous regions. Failing to jointly examine multi-region, multi-commodity price relationships limits researchers' ability to derive insights from increasingly high-dimensional price datasets of agrifood supply chains. We apply a machine-learning method - specifically, regularized regression - to augment the classical vector error correction model (VECM) and study large spatial-plus-vertical price systems. Leveraging weekly provincial-level data on the piglet-hog-pork supply chain in China, we uncover economically interesting changes in price relationships in the system before and after the outbreak of a major hog disease. To quantify price transmission in the large system, we rely on the spatial-plus-vertical price relationships identified by the regularized VECM to visualize comprehensive spatial and vertical price transmission of hypothetical shocks through joint impulse response functions. Price transmission shows considerable heterogeneity across regions and commodities as the VECM outcomes imply and display different dynamics over time. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.13967 |
| By: | Timoth\'ee Hornek Amir Sartipi; Igor Tchappi; Gilbert Fridgen |
| Abstract: | Accurate electricity price forecasting (EPF) is crucial for effective decision-making in power trading on the spot market. While recent advances in generative artificial intelligence (GenAI) and pre-trained large language models (LLMs) have inspired the development of numerous time series foundation models (TSFMs) for time series forecasting, their effectiveness in EPF remains uncertain. To address this gap, we benchmark several state-of-the-art pretrained models--Chronos-Bolt, Chronos-T5, TimesFM, Moirai, Time-MoE, and TimeGPT--against established statistical and machine learning (ML) methods for EPF. Using 2024 day-ahead auction (DAA) electricity prices from Germany, France, the Netherlands, Austria, and Belgium, we generate daily forecasts with a one-day horizon. Chronos-Bolt and Time-MoE emerge as the strongest among the TSFMs, performing on par with traditional models. However, the biseasonal MSTL model, which captures daily and weekly seasonality, stands out for its consistent performance across countries and evaluation metrics, with no TSFM statistically outperforming it. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.08113 |
| By: | Zhentao Shi; Jin Xi; Haitian Xie |
| Abstract: | This paper investigates the use of synthetic control methods for causal inference in macroeconomic settings when dealing with possibly nonstationary data. While the synthetic control approach has gained popularity for estimating counterfactual outcomes, we caution researchers against assuming a common nonstationary trend factor across units for macroeconomic outcomes, as doing so may result in misleading causal estimation-a pitfall we refer to as the spurious synthetic control problem. To address this issue, we propose a synthetic business cycle framework that explicitly separates trend and cyclical components. By leveraging the treated unit's historical data to forecast its trend and using control units only for cyclical fluctuations, our divide-and-conquer strategy eliminates spurious correlations and improves the robustness of counterfactual prediction in macroeconomic applications. As empirical illustrations, we examine the cases of German reunification and the handover of Hong Kong, demonstrating the advantages of the proposed approach. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.22388 |
| By: | Xueying Ding; Aakriti Mittal; Achintya Gopal |
| Abstract: | Time-series data is a vital modality within data science communities. This is particularly valuable in financial applications, where it helps in detecting patterns, understanding market behavior, and making informed decisions based on historical data. Recent advances in language modeling have led to the rise of time-series pre-trained models that are trained on vast collections of datasets and applied to diverse tasks across financial domains. However, across financial applications, existing time-series pre-trained models have not shown boosts in performance over simple finance benchmarks in both zero-shot and fine-tuning settings. This phenomenon occurs because of a i) lack of financial data within the pre-training stage, and ii) the negative transfer effect due to inherently different time-series patterns across domains. Furthermore, time-series data is continuous, noisy, and can be collected at varying frequencies and with varying lags across different variables, making this data more challenging to model than languages. To address the above problems, we introduce a Pre-trained MoDEL for FINance TimE-series (Delphyne). Delphyne achieves competitive performance to existing foundation and full-shot models with few fine-tuning steps on publicly available datasets, and also shows superior performances on various financial tasks. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.06288 |
| By: | Xin Jing (Yonsei University); Jin Seo Cho (Yonsei University) |
| Abstract: | This study exploits the quantile ARDL model to investigate the dynamic relationship between the confirmed COVID-19 cases and deaths in the U.S. following vaccination, with a focus on examining heterogeneity across different percentiles. The findings indicate that the confirmed case fatality rate decreased after vaccination, and the relationship between confirmed cases and deaths varies across different percentiles. |
| Keywords: | Quantile ARDL; Cointegration; COVID-19; Vaccination; Heterogeneity. |
| JEL: | C12 C22 I18 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2025rwp-247 |